Summary In the process of modern medical diagnosis, medical image‐assisted diagnosis plays a very important role. However, the process of medical image acquisition, will be affected by various types and degrees of noise, and there will be a certain probability of producing strip artifacts, which will interfere with the doctor's diagnosis, analysis, and treatment of diseases to a certain extent. However, the traditional medical image denoising method will cause problems such as image edge blurring and detail loss, and it is difficult to achieve the balance between noise removal and detail information retention. Therefore, denoising medical images and improving the accuracy of denoising as much as possible have very important scientific research significance and clinical application value. Based on this, this article proposes a medical image denoising method based on a double residual convolutional neural network and compares it with traditional medical images denoising methods such as K‐SVD, BM3D, and PNLM3. Experimental results show that the medical image denoising method based on the double residual convolutional neural network proposed in this article has excellent performance.

Off-road route planning plays a vital role across various domains, including civil transportation, earthquake relief and civil production. Off-road route planning requires the consideration of a diverse range of environmental constraints, which is a considerable challenge compared with conventional route planning along existing networks. We aimed to present a multi-objective route planning method for off-road environments. Specifically, we designed a heuristic function incorporating multiple characteristics to generate reliable route planning solutions based on Pareto optimality. We also constructed a hierarchical hexagonal grid model by merging similar grid cells and reconstructing adjacency relationships with other grid cells to achieve high efficiency. The proposed method was compared with conventional route planning methods using single- and multi-objective approaches in traditional and hierarchical hexagonal grid models. The proposed method can effectively plan off-road routes, improve optimal route distance and trafficability, and provide more efficient route solutions. It reduced the planning time by 97.1% compared with multi-objective route planning based on a traditional hexagonal grid model.

Collaborative optimization (CO), a decomposition-based multidisciplinary design optimization (MDO) method, is generally used in aeronautical systems. However, the subsystems optimizations of CO method are nested into system optimization of it in the execution process, numerous subsystem analyses are required to achieve consistency for entire system. For this problem, a nonnested collaborative optimization (NNCO) method is developed, in which system and subsystem optimizations are solved sequentially and repeatedly, and system consistancy is coordinated by a loop process together with the update of penalty parameters. Through test results of common analytical problems, which are generally used in the experiments of CO method, it's shown that the NNCO method requires two third fewer calls of the subsystem analysis than the CO method at least. Further more, CO and NNCO method are used to solve a satellite MDO problem, 0.05 and 8.05 hours are consumed for these two methods respectively. The efficiency of NNCO method in practical engineering design problem is proved.

Using the high fidelity model in the multidisciplinary design optimization (MDO) application is becoming more and more popular, but it brings complex coupling relationship and huge computing intensity, which need efforts to be well organized to reduce the computation time. In this paper, a parallel computing environment distributed in local area network (LAN) was established based on the collaborative optimization (CO) method. The distributed computing system communicated with Transfer Control Protocol (TCP) and used XML database to store and manage the data. The proposed system was applied in a remote sensing satellite system parameters design considering four subsystems, which includes structure, attitude control (AC), power, and thermal control (TC). The result shows that the efficiency can be improved by about 60% relative to the sequential running plan.

Parallel Packet Switch (PPS) is used intensively in today's terabit router to construct the switching fabric. Basic PPS equally deals with all of the traffic in order to achieve uniform load-balancing and high throughput, but it fails to support differentiated QoS. With the recent blooming of delay- sensitive Internet traffic, such as the peer-to-peer live streaming and IPTV, differentiated QoS is becoming an urgent demand. In this paper, we propose a novel and practical framework, the Differentiated Service Parallel Packet Switch (DS-PPS), which supports three fundamental QoS features: guaranteed-delay (GD), guaranteed-bandwidth (GB) and best-effort (BE). By adaptively adjusting the number of switching planes offered to each QoS class, DS-PPS precisely controls the delay bounds of GD traffic and the drop precedence of GB traffic. We evaluate DS-PPS by extensive theoretical analyses and comprehensive simulations. Experimental results on a prototype implementation of the framework show that DS-PPS outperforms the basic PPS in three main aspects. First, the average delay of TCP short packet under full load is reduced by more than 94%. Second, the average delay of real-time traffic under full load is reduced by more than 82%. And third, the GB traffic of low drop precedence is guaranteed of nearly three times the throughput of high drop precedence at the hotspots. Significantly, our proposed DS-PPS framework is universal and scalable to support various kinds of emerging QoS-sensitive applications in multi-service terabit routers without any extra overhead.

For a combined input and output queued (CIOQ) switch, maximal matching (MM) algorithm with a speedup of 2 has been known to deliver 100% throughput under any admissible traffic, where in each time slot, two independent schedulings and two cell transferrings are made respectively. This paper proposes a new kind of extended maximal matching (EMM) algorithm, which just utilizes a little information of VOQ length. We show that the implementation complexity of CIOQ switches is reduced by using the EMM(2) algorithm, where only one scheduling and two cell transferrings (data speedup of 2) are necessary in each time slot to achieve 100% throughput when input traffic is admissible. The EMM(2) algorithm can be easily realized by modifying the existing MM algorithms. In this paper, we provide a practical instance of EMM(2) algorithm, named EiSLIP(1,2), based on the well-known iSLIP algorithm. Simulations show that EiSLIP(1,2) algorithm with a data speedup of 2 achieves almost the same delay performance as output queued policy under uniform and non-uniform traffic with Bernoulli arrival, as well as the bursty traffic.

Although output queued (OQ) switches are prominent for their high performance, they are not easy to implement due to the high speedup requirement. Using a special scheduling algorithm in the first stage switch, a more scalable switch-memory-switch (SMS) architecture can emulate an OQ switch, where cells must be transferred from the inputs to the shared memories per time slot without arrival and departure conflicts. Although scheduling algorithm achieves good performance, the time complexity for constructing the bipartite graph is too high to be used in practice. In this paper, we propose a new iterative random round-Robin matching (iRRM) algorithm together with its constrained version CiRRM, where no bipartite graph is required to be constructed in advance to solve the departure conflict, and thus high computation overhead is avoided. In our algorithms, both the arrival and the departure conflicts are melted in the iterations. Each iterations consist of two steps: request step and grant step, where randomness and more easily implemented round-robin principle are used respectively. Through theoretical analysis, we obtain that with M=2/spl phi/(N-1) shared memories, where N is the port number and /spl phi/ is a constant larger than (2N-1)/(2N-2), iRRM/CiRRM can complete a matching within O(logM) iterations with high probability in M and the time complexity of CiRRM is only O(log/sup 2/M/loglogM), which is much lower than prior algorithms.